\section{Conclusion}
\label{conclusion}
In this paper, we introduce the motion-based HOOF feature and prove it as an another discriminative feature. The combination of HOG and HOOF support single use of HOG when the video quality is low. The result shows when the camera network is under severe condition of quality, the motion information is able to detect people from occlusion and large noises. The integral HOOF also can be used as motion information to lower the complexity of exhausting sliding window search by segmenting the interest region. By sharing the same computation component, histogram-based human detection has advantage of rapidly test other features in different scenario. Future work will include adding depth information and normalize the people by perspective scaling. 